Which Lag Length Selection Criteria Should We Employ?

Economics Bulletin, Vol. 3, No. 33, pp. 1−9, 2004

9 Pages Posted: 27 Feb 2006

Abstract

Estimating the lag length of autoregressive process for a time series is a crucial econometric exercise in most economic studies. This study attempts to provide helpfully guidelines regarding the use of lag length selection criteria in determining the autoregressive lag length. The most interesting finding of this study is that Akaike’s information criterion (AIC) and final prediction error (FPE) are superior than the other criteria under study in the case of small sample (60 observations and below), in the manners that they minimize the chance of under estimation while maximizing the chance of recovering the true lag length. One immediate econometric implication of this study is that as most economic sample data can seldom be considered “large” in size, AIC and FPE are recommended for the estimation the autoregressive lag length.

Keywords: lag length, selection criteria

Suggested Citation

Liew, Venus Khim-Sen, Which Lag Length Selection Criteria Should We Employ?. Economics Bulletin, Vol. 3, No. 33, pp. 1−9, 2004, Available at SSRN: https://ssrn.com/abstract=885505

Venus Khim-Sen Liew (Contact Author)

Universiti Malaysia Sarawak ( email )

Faculty of Economics and Business
Kota Samarahan, Sarawak 94300
Malaysia
+6082582415 (Phone)
+6082671794 (Fax)

HOME PAGE: http://www.feb.unimas.my/

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
2,341
Abstract Views
8,680
rank
7,012
PlumX Metrics